42 research outputs found
Achieving full diversity in multi-antenna two-way relay networks via symbol-based physical-layer network coding
No full textThis paper considers physical-layer network coding (PNC) with M-ary phase-shift keying (MPSK) modulation in two-way relay channel (TWRC). A low complexity detection technique, termed symbol-based PNC (SPNC), is proposed for the relay. In particular, attributing to the outer product operation imposed on the superposed MPSK signals at the relay, SPNC obtains the network-coded symbol (NCS) straightforwardly without having to detect individual symbols separately. Unlike the optimal multi-user detector (MUD) which searches over the combinations of all users’ modulation constellations, SPNC searches over only one modulation constellation, thus simplifies the NCS detection. Despite the reduced complexity, SPNC achieves full diversity in multi-antenna relay as the optimal MUD does. Specifically, antenna selection based SPNC (AS-SPNC) scheme and signal combining based SPNC (SC-SPNC) scheme are proposed. Our analysis of these two schemes not only confirms their full diversity performance, but also implies when SPNC is applied in multi-antenna relay, TWRC can be viewed as an effective single-input multiple-output (SIMO) system, in which AS-PNC and SC-PNC are equivalent to the general AS scheme and the maximal-ratio combining (MRC) scheme. Moreover, an asymptotic analysis of symbol error rate (SER) is provided for SC-PNC considering the case that the number of relay antennas is sufficiently large
CT Image Reconstruction by Spatial-Radon Domain Data-Driven Tight Frame Regularization
No full textThis paper proposes a spatial-Radon domain computed tomography (CT) image reconstruction model based on data-driven tight frames (SRD-DDTF). The proposed SRD-DDTF model combines the idea of the joint image and Radon domain inpainting model of Dong, Li, and Shen [J. Sci. Compd., 54 (2013), pp. 333-349] and that of the data-driven tight frames for image denoising [J.-F. Cai, H. Ji, Z. Shen, and G.-B. Ye, Appl. Compd. Harmon. Anal., 37 (2014), p. 89-105]. It is different from existing models in that both the CT image and its corresponding high quality projection image are reconstructed simultaneously using sparsity priors by tight frames that are adaptively learned from the data to provide optimal sparse approximations. An alternative minimization algorithm is designed to solve the proposed model, which is nonsmooth and nonconvex. Convergence analysis of the algorithm is provided. Numerical experiments show that the SRD-DDTF model is superior to the model of Dong, Li, and Shen [J. Sci. Compd., 54 (2013), pp. 333-349] especially in recovering some subtle structures in the images.Thousand Talents Plan of ChinaSCI(E)[email protected]; [email protected]
Post Reinforcement Learning Inference
No full textWe study estimation and inference using data collected by reinforcement learning (RL) algorithms. These algorithms adaptively experiment by interacting with individual units over multiple stages, updating their strategies based on past outcomes. Our goal is to evaluate a counterfactual policy after data collection and estimate structural parameters, such as dynamic treatment effects, that support credit assignment and quantify the impact of early actions on final outcomes. These parameters can often be defined as solutions to moment equations, motivating moment-based estimation methods developed for static data. In RL settings, however, data are often collected adaptively under nonstationary behavior policies. As a result, standard estimators fail to achieve asymptotic normality due to time-varying variance. We propose a weighted generalized method of moments (GMM) approach that uses adaptive weights to stabilize this variance. We characterize weighting schemes that ensure consistency and asymptotic normality of the weighted GMM estimators, enabling valid hypothesis testing and uniform confidence region construction. Key applications include dynamic treatment effect estimation and dynamic off-policy evaluation
Post Reinforcement Learning Inference
No full textWe consider estimation and inference using data collected from reinforcement
learning algorithms. These algorithms, characterized by their adaptive
experimentation, interact with individual units over multiple stages,
dynamically adjusting their strategies based on previous interactions. Our goal
is to evaluate a counterfactual policy post-data collection and estimate
structural parameters, like dynamic treatment effects, which can be used for
credit assignment and determining the effect of earlier actions on final
outcomes. Such parameters of interest can be framed as solutions to moment
equations, but not minimizers of a population loss function, leading to
Z-estimation approaches for static data. However, in the adaptive data
collection environment of reinforcement learning, where algorithms deploy
nonstationary behavior policies, standard estimators do not achieve asymptotic
normality due to the fluctuating variance. We propose a weighted Z-estimation
approach with carefully designed adaptive weights to stabilize the time-varying
estimation variance. We identify proper weighting schemes to restore the
consistency and asymptotic normality of the weighted Z-estimators for target
parameters, which allows for hypothesis testing and constructing uniform
confidence regions. Primary applications include dynamic treatment effect
estimation and dynamic off-policy evaluation
Functionalisation of Liquid Metal Droplets by Surface and Interface Engineering and Electrification
No full textLiquid metals, particularly gallium-based alloys, have attracted increasing attention as a class of functional materials due to their excellent electrical conductivity, fluidity, and tunable surface chemistry. This thesis explores strategies to engineer tunable interfaces in liquid metals through alloy composition modulation and particle functionalisation, ultimately enabling the development of reconfigurable electroluminescent display systems.
In Chapters 1 and 2, the background and fundamental principles of gallium-based liquid metals, surface modulation, and electric discharge-induced electroluminescence are introduced through a critical review of relevant literature.
In Chapter 3, the author investigates the influence of minor alloying elements (Sn, Bi, Zn) on the electrocapillarity and electrochemical surface behaviour of gallium (Ga). By systematically analysing how trace elements affect surface tension, wetting dynamics, and oxidation-induced structures, this study reveals the underlying mechanisms that govern surface reactivity and electric field responses in Ga-based liquid metals.
In Chapter 4, the author demonstrates a particle-functionalized liquid metal platform by embedding electroluminescent phosphor particles (e.g., ZnS:Cu) into the oxide shell of EGaIn droplets, forming liquid metal marbles. These marbles enable dynamic visualisation of electric discharge paths under applied voltage, offering a novel approach to spatially map and manipulate discharge trajectories.
In Chapter 5, building upon the discharge-path visualisation strategy established in Chapter 4, the author further develops a reconfigurable multi-colour display platform using phosphor-coated liquid metal marbles. This chapter extends the concept from single-colour discharge visualisation to full-spectrum, programmable colour display. By incorporating red, green, and blue phosphors in controlled ratios, additive colour mixing is achieved without the need for physically separated subpixels.
In Chapter 6, the thesis concludes by summarizing the major findings and outlining future research directions. The results collectively establish a unified interface engineering framework for gallium-based liquid metals, with potential applications ranging from fundamental electrochemical modulation to advanced soft optoelectronic systems
Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss
No full textLane detection is crucial for vehicle localization which makes it the foundation for automated driving and many intelligent and advanced driving assistant systems. Available vision-based lane detection methods do not make full use of the valuable features and aggregate contextual information, especially the interrelationships between lane lines and other regions of the images in continuous frames. To fill this research gap and upgrade lane detection performance, this paper proposes a pipeline consisting of self pre-training with masked sequential autoencoders and fine-tuning with customized PolyLoss for the end-to-end neural network models using multi-continuous image frames. The masked sequential autoencoders are adopted to pretrain the neural network models with reconstructing the missing pixels from a random masked image as the objective. Then, in the fine-tuning segmentation phase where lane detection segmentation is performed, the continuous image frames are served as the inputs, and the pre-trained model weights are transferred and further updated using the backpropagation mechanism with customized PolyLoss calculating the weighted errors between the output lane detection results and the labeled ground truth. Extensive experiment results demonstrate that, with the proposed pipeline, the lane detection model performance on both normal and challenging scenes can be advanced beyond the state-of-the art results, while the training time can be substantially shortened
Education as Wound and Witness: A Narrative Across Four Generations of Chinese Women
No full textThis article offers a gentle yet profound meditation on a family history that spans over a century, beginning with a long farewell to the author’s maternal grandmother, Laolao. Through the experiences of four generations of women in a Chinese family, the author presents a historical lens to examine the meaning of education within a broader sociocultural context. The author's mother, who received what was considered a modern education, emerges as the most disconnected and, in the author’s words, the most broken among the four generations.
The article critically explores the unintended consequences of education when wielded as a tool of dominance, highlighting how, rather than liberating, it can dehumanize and wound. Yet, amidst the rupture, the author offers a path toward reconciliation through the quiet power of seeing. By bearing witness to the past of an individual, a family, and even a society, one may awaken the capacity to forgive, to heal, and to reclaim agency.
Departing from traditional academic structure, this article unfolds through narrative. It begins with an intimate story and gradually zooms out, revealing a historical scroll that invites readers to embark on a shared journey of reflection and rediscovery. You are invited. Please be patient and enter the flow
Statistical Properties of Robust Satisficing
No full textThe Robust Satisficing (RS) model is an emerging approach to robust optimization, offering streamlined procedures and robust generalization across various applications. However, the statistical theory of RS remains unexplored in the literature. This paper fills in the gap by comprehensively analyzing the theoretical properties of the RS model. Notably, the RS structure offers a more straightforward path to deriving statistical guarantees compared to the seminal Distributionally Robust Optimization (DRO), resulting in a richer set of results. In particular, we establish two-sided confidence intervals for the optimal loss without the need to solve a minimax optimization problem explicitly. We further provide finite-sample generalization error bounds for the RS optimizer. Importantly, our results extend to scenarios involving distribution shifts, where discrepancies exist between the sampling and target distributions. Our numerical experiments show that the RS model consistently outperforms the baseline empirical risk minimization in small-sample regimes and under distribution shifts. Furthermore, compared to the DRO model, the RS model exhibits lower sensitivity to hyperparameter tuning, highlighting its practicability for robustness considerations.</p
Statistical Properties of Robust Satisficing
No full textThe Robust Satisficing (RS) model is an emerging approach to robust
optimization, offering streamlined procedures and robust generalization across
various applications. However, the statistical theory of RS remains unexplored
in the literature. This paper fills in the gap by comprehensively analyzing the
theoretical properties of the RS model. Notably, the RS structure offers a more
straightforward path to deriving statistical guarantees compared to the seminal
Distributionally Robust Optimization (DRO), resulting in a richer set of
results. In particular, we establish two-sided confidence intervals for the
optimal loss without the need to solve a minimax optimization problem
explicitly. We further provide finite-sample generalization error bounds for
the RS optimizer. Importantly, our results extend to scenarios involving
distribution shifts, where discrepancies exist between the sampling and target
distributions. Our numerical experiments show that the RS model consistently
outperforms the baseline empirical risk minimization in small-sample regimes
and under distribution shifts. Furthermore, compared to the DRO model, the RS
model exhibits lower sensitivity to hyperparameter tuning, highlighting its
practicability for robustness considerations
Distortion Agnostic Deep Watermarking
No full textWatermarking is the process of embedding information into an image that can survive under distortions, while requiring the encoded image to have little or no perceptual difference with the original image. Recently, deep learning-based methods achieved impressive results in both visual quality and message payload under a wide variety of image distortions. However, these methods all require differentiable models for the image distortions at training time, and may generalize poorly to unknown distortions. This is undesirable since the types of distortions applied to watermarked images are usually unknown and non-differentiable. In this paper, we propose a new framework for distortion-agnostic watermarking, where the image distortion is not explicitly modeled during training. Instead, the robustness of our system comes from two sources: adversarial training and channel coding. Compared to training on a fixed set of distortions and noise levels, our method achieves comparable or better results on distortions available during training, and better performance overall on unknown distortions.</p
